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Status Submitted
Created by Guest
Created on Sep 19, 2026

RELATED IDEAS

Training-free deferral router for uncertain agent outputs

Problem: Today's "learning to defer" systems must be retrained every time the human reviewer pool changes, so in practice teams either never retrain (and the router goes stale) or they defer everything to one overloaded expert. Uncertainty that can't be routed cheaply gets ignored.

Idea: Add a watsonx Orchestrate agent skill that uses conformal prediction sets to spot label-level uncertainty in an agent's output and routes each uncertain case to whichever available human reviewer best discriminates the remaining plausible options — with no retraining when reviewers join or leave. Grounded in Bary, Macq & Petit (2025), who showed this training-free approach beating both the standalone model and the best single expert while cutting expert workload up to 11x.

Value: Enterprises get a deferral router that stays accurate as teams change, fewer wrong agent actions reaching customers, and far less wasted senior-reviewer time.

Idea priority Low
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